Algebraic Subgraph Counting
Summary: ASC extends candidate-tree counting with matrix algebra to enforce non-tree edges, then locally samples to handle injectivity. This non-learning approach delivers accurate, stable counts at billion-edge scale, outperforming learning-based FlowSC by over 10×. (summarized by gpt-6-luna on Oct 22 2026)
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Authors
- 1. Qiuyu Guo (University of New South Wales)
- 2. Jianye Yang (Guangdong University of Technology; Peng Cheng Laboratory)
- 3. Wenjie Zhang (University of New South Wales)
- 4. Hanchen Wang (University of Technology Sydney)
- 5. Ying Zhang (Zhejiang Gongshang University)
- 6. Xuemin Lin (Chinese University of Hong Kong)
BibTeX Citation
@inproceedings{guo_sigmod27,
title = {{Algebraic Subgraph Counting}},
author = {Guo, Qiuyu and Yang, Jianye and Zhang, Wenjie and Wang, Hanchen and Zhang, Ying and Lin, Xuemin},
series = {{SIGMOD} '27},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3837094},
url = {https://dl.acm.org/doi/10.1145/3837094},
year = {2027}
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